The QLM model is optimized to maximize the likelihood of generating questions observed in training data. Thus, it tends to generate common questions, which might not be effective to clarify search intent for IR.
To address the issue, the QCM model is proposed to generate clarifying questions by maximizing a clarification utility function.
The model is obtained by continually training the QLM model to maximize the expected clarification utility [1]}, [2]}, defined as
\(\mathbb {E}_{Q^{\prime } \sim P_r(Q^{\prime }|Q,\mathcal {H}; \theta )} [\mathbb {U}(Q^{\prime },Q)].\) 
